Over the last five years, mobile health applications (mHealthapp) have evolved exponentially to assess and support our health and well-being. This paper presents an Artificial Intelligence (AI)-enabledmHealth app rating tool which takes multidimensional measures such as starrating, user’s review and features declared by the developer to generate apprating. However, currently, there is very little conceptual understanding onhow users’ reviews affect app rating from a multi-dimensional perspective. This study applies artificial intelligence (AI)-based text mining technique to develop more comprehensive understanding of users’ feedback based on an array of factors, determining the mHealth app ratings. Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user’s star rating, user’s text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Our proposed model concentrates on heart related apps found in the play store and app gallery. The findings indicate the efficacy of the model as opposed to the current device scale. This study has implications for both app developers and consumers who are using mHealth apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has been also found that the fuzzy based rating has a high variance compared to the conventional app rating whereas the fuzzy based rating shows high relationship in contrast to scoring based on expert opinion.